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Security in the Cloud: Safeguarding Indic Language Data with Large Language Models

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By Mohammed Imran K R, CTO, E2E Networks Ltd 

Large Language Models (LLMs) have been all the rage in 2023. These AI models, with their ability to generate human-like text and follow instructions in human language, have captivated users and hold potential applications in numerous domains, ranging from customer support to education and beyond. They have demonstrated proficiency in handling tasks such as language translation, question-answering, text summarization, and conversational AI. This points towards a paradigm shift in how human-machine interfaces might be built in the future.

The fascinating aspect of LLMs lies in how they are built. Their architecture incorporates advanced machine learning (ML) algorithms, particularly transformers. These models undergo training on extensive datasets of text across advanced cloud GPUs, on a broad spectrum of subjects, languages, and styles. During training, the model learns to predict the next word in a sentence based on the context provided by preceding words. This capability, developed through training and fine-tuning of a model to a massive number of datapoints from the datasets, enables the model to grasp nuanced aspects of language, including syntax, semantics, and knowledge.

The nature of LLMs and their training approach means that the choice and quality of the dataset determines how the models eventually behave. For instance, if an LLM is trained with a dataset tailored for a specific cultural context, the model’s responses will primarily be suitable for that context. It would also adopt the nuances of language, knowledge, and semantics reflective of the original dataset.

This is a critical factor for a country like India, with its rich cultural and linguistic diversity. Indian languages, including Hindi, Bengali, Gujarati, Telugu, Marathi, Tamil, and Urdu, are spoken by millions across the country and an ever-expanding diaspora. Each language has its distinct script, literary heritage, and regional variations. To harness LLM capabilities for India’s vast user base, we need to develop Indic-language foundational models, trained on diverse datasets specific to each language. While the core technology of building LLMs remains constant, the datasets must vary based on the language.

Since dataset purity is key, security of the dataset before and during training becomes paramount in this context. As these datasets comprise millions, or even billions of data points from various public sources of Indian content, they must be managed in full compliance with Indian laws. During the training process, advanced cloud GPUs, such as the AI supercomputer HGX 8xH100 that E2E Cloud has pioneered in India, are employed. This AI training platform, powered by H100 GPUs, is capable of handling trillion parameter AI models and is designed for building foundational language models. Training time for foundational language models can range from days to months, and throughout this process, the security of the model and dataset is crucial. The hyperscale cloud platform used for training must be impervious to foreign intrusion. Furthermore, they should be fully compliant with Indian IT laws. This chooses cloud provider a critical decision.

LLMs are also susceptible to prompt poisoning, a technique where attackers manipulate the training process by introducing adversarial prompts with toxic or biased content. If these prompts are included in the model’s training data, they can drastically affect the LLM’s output. For example, attackers could insert prompts that cause the LLM to ignore certain user inputs or generate offensive text, posing significant risks once the LLM is deployed. To mitigate such risks, the dataset and training process must be conducted in a highly secure and protected environment. Secure hyperscale cloud GPU platforms, specifically built in India with India-centric security and privacy compliances, therefore become indispensable.

For anyone building Indic-language LLMs, key decision factors, therefore, are several. First and foremost, choosing CSPs who offer instant access to advanced GPU platforms like the HGX 8xH100 AI Supercomputer can help cut down training time, while offering cutting edge capabilities. Secondly, the CSP should be 100% compliant with Indian IT laws. Finally, the datasets used should be diverse and free from biases, and reflect the cultural nuances of the language.

The development and implementation of Indic-language LLMs represent a massive opportunity for India. These AI models, tailored specifically to the linguistic diversity and cultural richness of the Indian subcontinent, have the potential to unlock unparalleled benefits for the vast and growing Indian user base. By training these models on diverse, region-specific datasets, we can ensure that they not only understand but also resonate with the dialects, languages, and cultural nuances of India. This localized approach to LLMs stands to transform various sectors such as education, customer service, and technology.

The key to harnessing the full potential of these LLMs lies in their responsible development and deployment, ensuring they are built within secure, law-compliant environments. By focusing on these aspects, we can empower the Indian populace with AI tools that are not just technologically advanced but also culturally attuned and ethically grounded, thus truly harnessing the transformative power of LLMs for India’s unique and diverse needs.

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